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Record W2900095041 · doi:10.1101/466250

Crops and the seed mass-seed output trade-off in plants

2018· preprint· en· W2900095041 on OpenAlexaff
Adam R. Martin

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2018
Typepreprint
Languageen
FieldAgricultural and Biological Sciences
TopicSoybean genetics and cultivation
Canadian institutionsThe Scarborough HospitalUniversity of Toronto
Fundersnot available
KeywordsDomesticationTraitBiologyCropSelection (genetic algorithm)Plant evolutionPlant speciesAgronomyBotanyEcologyGene

Abstract

fetched live from OpenAlex

Abstract A trade-off between seed mass (SM) and seed output (SO) defines a central axis of ecological variation among plants, with implications for understanding both plant trait evolution and plant responses to environmental change. While an observed negative SM-SO relationship is hypothesized to reflect universal constraints on resource allocation in all plants, domestication has likely fundamentally altered this relationship. Using a dataset of SM and SO for 41 of the world most widespread crops and 1,190 wild plant species, coupled with observational data on these traits in soy ( Glycine max ) and maize ( Zea mays ), I show that domestication has systematically rewired SM-SO relationships in crops. Compared to wild plants, virtually all crops express a higher SM for a given SO; this domestication signature is especially prominent in seed crops, and also influences the phylogenetic signal in SM and SO. In maize these traits have become positively related likely due to simultaneous selection for greater SM and SO, while in soy these traits have become decoupled likely due to primary selection for SM only. Evolved relationships between SM and SO in plants have been disrupted by both conscious and unconscious artificial selection, which represents a key aspect of how the functional biology of crops differ fundamentally from wild plants along “universal” plant trait spectra.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.851
Threshold uncertainty score0.550

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.017
GPT teacher head0.199
Teacher spread0.181 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2018
Admission routes1
Has abstractyes

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